Protein Threading using Parallel Evolution Strategy

The protein threading problem is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this paper, we propose an evolution strategy for protein threading. We also developed two parallel approaches for fast threading. We have obtained at least better results than current comparable approaches, as well as significant reduction in execution time.

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term “protein threading.” These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

This chapter (Protein folding software) presents large pools of folded structures as determined by X-ray diffraction crystallography. The amino acid sequences for which three-dimensional structure has been ascertained are used to align the amino acid sequences in other proteins and determine their three-dimensional structures. The known structures are compiled in protein libraries such as the Proton Data Bank (PDB) and SWISS-PROT database. BLAST is a computer program that searches the data bank of proteins that have known structure and aligns the amino acid sequences in similar proteins. The efficacy of programs that determine the structure from known structures are assessed by CASP. The above type of analysis is called homology modeling. Protein threading is also described in this chapter. Ab initio modeling is used when there are few or no proteins for the comparison of protein structures. Transition state theory, similar to the transition state as used in organic/inorganic chemistry, is explained. Molecular dynamics, a powerful tool used for the investigation of protein folding, is described. Also Monte Carlo Simulations and Markov State Models are discussed in this chapter. We further explore amino acids and their involvement in α-helical and β-pleated sheets. Β-barrel proteins using hyperboloid computer programs are described. Other computer programs such as QUARK, I-TASSER, Modeller, Amber, and the Hidden Markov Model are also explained.

In our protein structure prediction computer program RAPTOR, we have implemented a linear programming (LP) approach to protein threading based on the template contact map graph. Our protein threading model considers pairwise contact potential rigorously. In order to further improve the computational efficiency of our LP approach, this paper proposes a graph reduction technique to reduce a template contact graph to a new one with fewer vertices and edges. Our graph reduction operation is formalized to minimize the number of variables, constraints and non-zero elements in the constraint matrix of our linear programs. These three factors are key to the computational time of solving linear programs by both the Simplex method and the Interior-Point method. This graph reduction technique does not impact the quality of protein threading (i.e., the energy function is still globally optimized). Experiments show that the more a template contact graph can be reduced, the more computational efficiency improvement can be attained and that in average, the computational efficiency of threading any long sequence to the whole template database can be improved by 30%.

In protein threading, one is given a protein sequence, together with a database of protein core structures that may contain the natural structure of the sequence. The object of protein threading is to correctly identify the structure(s) corresponding to the sequence. Since the core structures are already associated with specific biological functions, threading has the potential to provide biologists with useful insights about the function of a newly discovered protein sequence. Statistical tests for threading results based on the theory of extreme values suggest several combinatorial problems. For example, what is the number of waysm′=#t{Li>xi}i=0n of choosing a sequence {Xi}i=1n from the set {1, 2, ...,t}, subject to the difference constraints {Li=Xi+1−Xi>xi}i=0n, whereX0=0,Xn+1=t+1, and {xi}i=0n is an arbitrary sequence of integers? The quantitym′ has many attractive combinatorial interpretations and reduces in special continuous limits to a probabilistic formula discovered by the Finetti. Just as many important probabilities can be derived from de Finetti's formula, many interesting combinatorial quantities can be derived fromm′. Empirical results presented here show that the combinatorial approach to threading statistics appears promising, but that structural periodicities in proteins and energetically unimportant structure elements probably introduce statistical correlations that must be better understood.

Local structure prediction can facilitate ab initio structure prediction, protein threading, and remote homology detection. However, the accuracy of existing methods is limited. In this paper, we propose a knowledge-based prediction method that assigns a measure called the local match rate to each position of an amino acid sequence to estimate the confidence of our method. Empirically, the accuracy of the method correlates positively with the local match rate; therefore, we employ it to predict the local structures of positions with a high local match rate. For positions with a low local match rate, we propose a neural network prediction method. To better utilize the knowledge-based and neural network methods, we design a hybrid prediction method, HYPLOSP (HYbrid method to Protein LOcal Structure Prediction) that combines both methods. To evaluate the performance of the proposed methods, we first perform cross-validation experiments by applying our knowledge-based method, a neural network method, and HYPLOSP to a large dataset of 3,925 protein chains. We test our methods extensively on three different structural alphabets and evaluate their performance by two widely used criteria, Maximum Deviation of backbone torsion Angle (MDA) and Q(N), which is similar to Q(3) in secondary structure prediction. We then compare HYPLOSP with three previous studies using a dataset of 56 new protein chains. HYPLOSP shows promising results in terms of MDA and Q(N) accuracy and demonstrates its alphabet-independent capability.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Chapter 1 of this thesis provides an overview of the structure and function of proteins as well as their investigation via NMR spectroscopy. Proteins are one of the most important building blocks of life. They are part of every organism and fulfill a variety of functions including, for example, the catalysis of many chemical reactions, which is the reason various processes in an organism become possible in the first place. Proteins are made up of amino acids, which are linked together via peptide bonds during the protein biosynthesis. The 22 proteinogenic amino acids result in an almost unlimited number of sequential combinations that give proteins their versatility. The sequence of amino acids in a protein is also called the primary structure. From this, locally limited structural motifs such as α-helices and β-sheets are formed, representing the secondary structure. Further intramolecular interactions create the tertiary structure of a protein, which can be seen as the overall three-dimensional structure of the protein. Furthermore, several individual proteins can assemble into a larger complex to form a quaternary structure. There are various methods for investigating the structure of proteins, including nuclear magnetic resonance (NMR) spectroscopy. In NMR spectroscopy, atomic nuclei with a non-zero nuclear spin are polarized by a constant magnetic field and then excited using radio frequency pulses. The emitted resonance frequencies are detected in the form of a free induction decay (FID) and converted into an NMR spectrum by a Fourier transformation. Many NMR experiments are available for researching proteins. By default, a two-dimensional 1H15N-HSQC spectrum is measured, which provides signals for each N-H bond in a protein meaning that one signal is obtained for each N-H in the backbone of the amino acids. Using 3D NMR experiments, these signals can then be assigned to the respective amino acids in the protein sequence. This makes it possible to investigate the dynamics of proteins as well as the formation of complexes with a ligand or another biomacromolecule if they interact. Further 3D NMR experiments also make it possible to assign the side-chains of the protein. With the help of the determined resonance assignments and spatial information from various NOESY spectra, a protein structure can then be calculated using a suitable computer program. Recently, the prediction of protein structures using AI (artificial intelligence)-supported programs has also become more prevalent. In particular, the artificial neural network AlphaFold has been extremely successful in predicting correct structures without any experimental data provided by the user. Chapter 2 of this thesis introduces the research field of small proteins. These are proteins with a length of usually less than 100 amino acids, although definitions in literature vary and upper limits of, for example, 50 or 200 amino acids exist as well. Various other names are also used to describe small proteins, such as μ-proteins or sORF-encoded peptides. The field of small proteins is still relatively new, as they were overlooked in research for many years. One reason for this was the lower limit of 100 codons that was set for the identification of open reading frames (ORFs) in genome projects. This served to exclude numerous biologically irrelevant ORFs from genome annotation. However, this meant that functional ORFs coding for small proteins were also ignored. Another reason was that experimental methods were not designed for the analysis of small proteins, meaning they could often not be detected at all. However, new analytical methods such as ribosome profiling and a generally increased focus on the detection of small proteins have led to their detection in all domains of life. In the research group of Prof. Dr. Schwalbe, small proteins of various prokaryotes were structurally characterized as part of a special priority program by the DFG. NMR spectroscopy was primarily used for this purpose, as it is the method of choice for the investigation of small proteins. The results of this research were published in several articles, two of which are part of this dissertation.

BackgroundAs genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.ResultsTo meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.ConclusionsThese tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

The structural characterization of protein sequences is one of the most common problems in biology. This task is usually facilitated by the accurate Three-Dimensional (3-D) structure of the protein. This paper was carried out to investigate and compare the most suitable software online tools for homology modeling of protein structure by obtaining few samples of proteins. Different length, known and unknown (3-D) proteins structures of bacteria, and viruses were obtained from Uniprot which do not have a Protein Data Bank Identifier (PDB ID). This paper used several tools, such CPHmodels-3.2, Phyre2, Swiss- Model, (PS)2-v2, and Robetta, for predicting, comparison and model generation, where CPHmodels-3.2 and Phyre2 Server, gave relatively better values. Proteins (3-D) structure was evaluated using PROSESS Server and the better evaluation to proteins structure was for the Phyre2 software online tool.

MotivationTarget discovery is crucial in drug development, especially for complex chronic diseases. Recent advances in high-throughput technologies and the explosion of biomedical data have highlighted the potential of computational druggability prediction methods. However, most current methods rely on sequence-based features with machine learning, which often face challenges related to hand-crafted features, reproducibility, and accessibility. Moreover, the potential of raw sequence and protein structure has not been fully investigated.ResultsHere, we leveraged both protein sequence and structure using deep learning techniques, revealing that protein sequence, especially pre-trained embeddings, is more informative than protein structure. Next, we developed DrugTar, a high-performance deep learning algorithm integrating sequence embeddings from the ESM-2 pre-trained protein language model with gene ontologies to predict druggability. DrugTar achieved areas under the curve and precision–recall curve values of 0.94, outperforming state-of-the-art methods. In conclusion, DrugTar streamlines target discovery as a bottleneck in developing novel therapeutics.Availability and implementationDrugTar is available as a web server at www.DrugTar.com. The data and source code are at https://github.com/NBorhani/DrugTar.

An integrated approach to the analysis and modeling of protein sequences and structures. III. A comparative study of sequence conservation in protein structural families using multiple structural alignments

While protein engineering, which iteratively optimizes protein fitness by screening the gigantic mutational space, is constrained by experimental capacity, various machine learning models have substantially expedited protein engineering. Three-dimensional protein structures promise further advantages, but their intricate geometric complexity hinders their applications in deep mutational screening. Persistent homology, an established algebraic topology tool for protein structural complexity reduction, fails to capture the homotopic shape evolution during the filtration of a given data. This work introduces a Topology-offered protein Fitness (TopFit) framework to complement protein sequence and structure embeddings. Equipped with an ensemble regression strategy, TopFit integrates the persistent spectral theory, a new topological Laplacian, and two auxiliary sequence embeddings to capture mutation-induced topological invariant, shape evolution, and sequence disparity in the protein fitness landscape. The performance of TopFit is assessed by 34 benchmark datasets with 128,634 variants, involving a vast variety of protein structure acquisition modalities and training set size variations.

Cold plasma processing of sesame milk: Changes on physicochemical properties, nutrition, anti-nutritional factors, protein structure and allergenicity

2 - The Physiological Structure and Function of Proteins